Neuroscientists create a comprehensive map of the cerebral cortex

By analyzing brain scans taken as people watched movie clips, MIT researchers have created the most comprehensive map yet of the functions of the brain’s cerebral cortex.

Using functional magnetic resonance imaging (fMRI) data, the research team identified 24 networks with different functions, which include processing language, social interactions, visual features, and other types of sensory input.

Many of these networks have been seen before but haven’t been precisely characterized using naturalistic conditions. While the new study mapped networks in subjects watching engaging movies, previous works have used a small number of specific tasks or examined correlations across the brain in subjects who were simply resting.

“There’s an emerging approach in neuroscience to look at brain networks under more naturalistic conditions. This is a new approach that reveals something different from conventional approaches in neuroimaging,” says Robert Desimone, director of MIT’s McGovern Institute for Brain Research. “It’s not going to give us all the answers, but it generates a lot of interesting ideas based on what we see going on in the movies that's related to these network maps that emerge.”

The researchers hope that their new map will serve as a starting point for further study of what each of these networks is doing in the brain.

Desimone and John Duncan, a program leader in the MRC Cognition and Brain Sciences Unit at Cambridge University, are the senior authors of the study, which appears today in Neuron. Reza Rajimehr, a research scientist in the McGovern Institute and a former graduate student at Cambridge University, is the lead author of the paper.

Precise mapping

The cerebral cortex of the brain contains regions devoted to processing different types of sensory information, including visual and auditory input. Over the past few decades, scientists have identified many networks that are involved in this kind of processing, often using fMRI to measure brain activity as subjects perform a single task such as looking at faces.

In other studies, researchers have scanned people’s brains as they do nothing, or let their minds wander. From those studies, researchers have identified networks such as the default mode network, a network of areas that is active during internally focused activities such as daydreaming.

“Up to now, most studies of networks were based on doing functional MRI in the resting-state condition. Based on those studies, we know some main networks in the cortex. Each of them is responsible for a specific cognitive function, and they have been highly influential in the neuroimaging field,” Rajimehr says.

However, during the resting state, many parts of the cortex may not be active at all. To gain a more comprehensive picture of what all these regions are doing, the MIT team analyzed data recorded while subjects performed a more natural task: watching a movie.

“By using a rich stimulus like a movie, we can drive many regions of the cortex very efficiently. For example, sensory regions will be active to process different features of the movie, and high-level areas will be active to extract semantic information and contextual information,” Rajimehr says. “By activating the brain in this way, now we can distinguish different areas or different networks based on their activation patterns.”

The data for this study was generated as part of the Human Connectome Project. Using a 7-Tesla MRI scanner, which offers higher resolution than a typical MRI scanner, brain activity was imaged in 176 people as they watched one hour of movie clips showing a variety of scenes.

The MIT team used a machine-learning algorithm to analyze the activity patterns of each brain region, allowing them to identify 24 networks with different activity patterns and functions.

Some of these networks are located in sensory areas such as the visual cortex or auditory cortex, as expected for regions with specific sensory functions. Other areas respond to features such as actions, language, or social interactions. Many of these networks have been seen before, but this technique offers more precise definition of where the networks are located, the researchers say.

“Different regions are competing with each other for processing specific features, so when you map each function in isolation, you may get a slightly larger network because it is not getting constrained by other processes,” Rajimehr says. “But here, because all the areas are considered together, we are able to define more precise boundaries between different networks.”

The researchers also identified networks that hadn’t been seen before, including one in the prefrontal cortex, which appears to be highly responsive to visual scenes. This network was most active in response to pictures of scenes within the movie frames.

Executive control networks

Three of the networks found in this study are involved in “executive control,” and were most active during transitions between different clips. The researchers also observed that these control networks appear to have a “push-pull” relationship with networks that process specific features such as faces or actions. When networks specific to a particular feature were very active, the executive control networks were mostly quiet, and vice versa.

“Whenever the activations in domain-specific areas are high, it looks like there is no need for the engagement of these high-level networks,” Rajimehr says. “But in situations where perhaps there is some ambiguity and complexity in the stimulus, and there is a need for the involvement of the executive control networks, then we see that these networks become highly active.”

Using a movie-watching paradigm, the researchers are now studying some of the networks they identified in more detail, to identify subregions involved in particular tasks. For example, within the social processing network, they have found regions that are specific to processing social information about faces and bodies. In a new network that analyzes visual scenes, they have identified regions involved in processing memory of places.

“This kind of experiment is really about generating hypotheses for how the cerebral cortex is functionally organized. Networks that emerge during movie watching now need to be followed up with more specific experiments to test the hypotheses. It’s giving us a new view into the operation of the entire cortex during a more naturalistic task than just sitting at rest,” Desimone says.

The research was funded by the McGovern Institute, the Cognitive Science and Technology Council of Iran, the MRC Cognition and Brain Sciences Unit at the University of Cambridge, and a Cambridge Trust scholarship.

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A portable light system that can digitize everyday objects

When Nikola Tesla predicted we’d have handheld phones that could display videos, photographs, and more, his musings seemed like a distant dream. Nearly 100 years later, smartphones are like an extra appendage for many of us.

Digital fabrication engineers are now working toward expanding the display capabilities of other everyday objects. One avenue they’re exploring is reprogrammable surfaces — or items whose appearances we can digitally alter — to help users present important information, such as health statistics, as well as new designs on things like a wall, mug, or shoe.

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), the University of California at Berkeley, and Aarhus University have taken an intriguing step forward by fabricating “PortaChrome,” a portable light system and design tool that can change the color and textures of various objects. Equipped with ultraviolet (UV) and red, green, and blue (RGB) LEDs, the device can be attached to everyday objects like shirts and headphones. Once a user creates a design and sends it to a PortaChrome machine via Bluetooth, the surface can be programmed into multicolor displays of health data, entertainment, and fashion designs.

To make an item reprogrammable, the object must be coated with photochromic dye, an invisible ink that can be turned into different colors with light patterns. Once it’s coated, individuals can create and relay patterns to the item via the team’s graphic design software, or use the team’s API to interact with the device directly and embed data-driven designs. When attached to a surface, PortaChrome’s UV lights saturate the dye while the RGB LEDs desaturate it, activating the colors and ensuring each pixel is toned to match the intended design.

Zhu and her colleagues’ integrated light system changes objects’ colors in less than four minutes on average, which is eight times faster than their prior work, “Photo-Chromeleon.” This speed boost comes from switching to a light source that makes contact with the object to transmit UV and RGB rays. Photo-Chromeleon used a projector to help activate the color-changing properties of photochromic dye, where the light on the object's surface is at a reduced intensity.

“PortaChrome provides a more convenient way to reprogram your surroundings,” says Yunyi Zhu ’20, MEng ’21, an MIT PhD student in electrical engineering and computer science, affiliate of CSAIL, and lead author on a paper about the work. “Compared with our projector-based system from before, PortaChrome is a more portable light source that can be placed directly on top of the photochromic surface. This allows the color change to happen without user intervention and helps us avoid contaminating our environment with UV. As a result, users can wear their heart rate chart on their shirt after a workout, for instance.”

Giving everyday objects a makeover

In demos, PortaChrome displayed health data on different surfaces. A user hiked with PortaChrome sewed onto their backpack, putting it into direct contact with the back of their shirt, which was coated in photochromic dye. Altitude and heart rate sensors sent data to the lighting device, which was then converted into a chart through a reprogramming script developed by the researchers. This process created a health visualization on the back of the user’s shirt. In a similar showing, MIT researchers displayed a heart gradually coming together on the back of a tablet to show how a user was progressing toward a fitness goal.

PortaChrome also showed a flair for customizing wearables. For example, the researchers redesigned some white headphones with sideways blue lines and horizontal yellow and purple stripes. The photochromic dye was coated on the headphones and the team then attached the PortaChrome device to the inside of the headphone case. Finally, the researchers successfully reprogrammed their patterns onto the object, which resembled watercolor art. Researchers also recolored a wrist splint to match different clothes using this process.

Eventually, the work could be used to digitize consumers’ belongings. Imagine putting on a cloak that can change your entire shirt design, or using your car cover to give your vehicle a new look.

PortaChrome’s main ingredients

On the hardware end, PortaChrome is a combination of four main ingredients. Their portable device consists of a textile base as a sort of backbone, a textile layer with the UV lights soldered on and another with the RGB stuck on, and a silicone diffusion layer to top it off. Resembling a translucent honeycomb, the silicone layer covers the interlaced UV and RGB LEDs and directs them toward individual pixels to properly illuminate a design over a surface.

This device can be flexibly wrapped around objects with different shapes. For tables and other flat surfaces, you could place PortaChrome on top, like a placemat. For a curved item like a thermos, you could wrap the light source around like a coffee cup sleeve to ensure it reprograms the entire surface.

The portable, flexible light system is crafted with maker space-available tools (like laser cutters, for example), and the same method can be replicated with flexible PCB materials and other mass manufacturing systems.

While it can also quickly convert our surroundings into dynamic displays, Zhu and her colleagues believe it could benefit from further speed boosts. They'd like to use smaller LEDs, with the likely result being a surface that could be reprogrammed in seconds with a higher-resolution design, thanks to increased light intensity.

“The surfaces of our everyday things are encoded with colors and visual textures, delivering crucial information and shaping how we interact with them,” says Georgia Tech postdoc Tingyu Cheng, who was not involved with the research. “PortaChrome is taking a leap forward by providing reprogrammable surfaces with the integration of flexible light sources (UV and RGB LEDs) and photochromic pigments into everyday objects, pixelating the environment with dynamic color and patterns. The capabilities demonstrated by PortaChrome could revolutionize the way we interact with our surroundings, particularly in domains like personalized fashion and adaptive user interfaces. This technology enables real-time customization that seamlessly integrates into daily life, offering a glimpse into the future of ‘ubiquitous displays.’”

Zhu is joined by nine CSAIL affiliates on the paper: MIT PhD student and MIT Media Lab affiliate Cedric Honnet; former visiting undergraduate researchers Yixiao Kang, Angelina J. Zheng, and Grace Tang; MIT undergraduate student Luca Musk; University of Michigan Assistant Professor Junyi Zhu SM ’19, PhD ’24; recent postdoc and Aarhus University assistant professor Michael Wessely; and senior author Stefanie Mueller, the TIBCO Career Development Associate Professor in the MIT departments of Electrical Engineering and Computer Science and Mechanical Engineering and leader of the HCI Engineering Group at CSAIL.

This work was supported by the MIT-GIST Joint Research Program and was presented at the ACM Symposium on User Interface Software and Technology in October.

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Startup gives surgeons a real-time view of breast cancer during surgery

Breast cancer is the second most common type of cancer and cause of cancer death for women in the United States, affecting one in eight women overall.

Most women with breast cancer undergo lumpectomy surgery to remove the tumor and a rim of healthy tissue surrounding the tumor. After the procedure, the removed tissue is sent to a pathologist to look for signs of disease at the edge of the tissue assessed. Unfortunately, about 20 percent of women who have lumpectomies must undergo a second surgery to remove more tissue.

Now, an MIT spinout is giving surgeons a real-time view of cancerous tissue during surgery. Lumicell has developed a handheld device and an optical imaging agent that, when combined, allow surgeons to scan the tissue within the surgical cavity to visualize residual cancer cells.  The surgeons see these images on a monitor that can guide them to remove additional tissue during the procedure.

In a clinical trial of 357 patients, Lumicell’s technology not only reduced the need for second surgeries but also revealed tissue suspected to contain cancer cells that may have otherwise been missed by the standard of care lumpectomy.

The company received U.S. Food and Drug Administration approval for the technology earlier this year, marking a major milestone for Lumicell and the founders, who include MIT professors Linda Griffith and Moungi Bawendi along with PhD candidate W. David Lee ’69, SM ’70. Much of the early work developing and testing the system took place at the Koch Institute for Integrative Cancer Research at MIT, beginning in 2008.

The FDA approval also held deep personal significance for some of Lumicell’s team members, including Griffith, a two-time breast cancer survivor, and Lee, whose wife’s passing from the disease in 2003 changed the course of his life.

An interdisciplinary approach

Lee ran a technology consulting group for 25 years before his wife was diagnosed with breast cancer. Watching her battle the disease inspired him to develop technologies that could help cancer patients.

His neighbor at the time was Tyler Jacks, the founding director of the Koch Institute. Jacks invited Lee to a series of meetings at the Koch involving professors Robert Langer and Bawendi, and Lee eventually joined the Koch Institute as an integrative program officer in 2008, where he began exploring an approach for improving imaging in living organisms with single-cell resolution using charge-coupled device (CCD) cameras.

“CCD pixels at the time were each 2 or 3 microns and spaced 2 or 3 microns,” Lee explains. “So the idea was very simple: to stabilize a camera on a tissue so it would move with the breathing of the animal, so the pixels would essentially line up with the cells without any fancy magnification.”

That work led Lee to begin meeting regularly with a multidisciplinary group including Lumicell co-founders Bawendi, currently the Lester Wolfe Professor of Chemistry at MIT and winner of the 2023 Nobel Prize in Chemistry; Griffith, the School of Engineering Professor of Teaching Innovation in MIT’s Department of Biological Engineering and an extramural faculty member at the Koch Institute; Ralph Weissleder, a professor at Harvard Medical School; and David Kirsch, formerly a postdoc at the Koch Institute and now a scientist at the Princess Margaret Cancer Center.

“On Friday afternoons, we’d get together, and Moungi would teach us some chemistry, Lee would teach us some engineering, and David Kirsch would teach some biology,” Griffith recalls.

Through those meetings, the researchers began to explore the effectiveness of combining Lee’s imaging approach with engineered proteins that would light up where the immune system meets the edge of tumors, for use during surgery. To begin testing the idea, the group received funding from the Koch Institute Frontier Research Program via the Kathy and Curt Marble Cancer Research Fund.

“Without that support, this never would have happened,” Lee says. “When I was learning biology at MIT as an undergrad, genetics weren’t even in the textbooks yet. But the Koch Institute provided education, funding, and most importantly, connections to faculty, who were willing to teach me biology.”

In 2010, Griffith was diagnosed with breast cancer.

“Going through that personal experience, I understood the impact that we could have,” Griffith says. “I had a very unusual situation and a bad kind of tumor. The whole thing was nerve-wracking, but one of the most nerve-wracking times was waiting to find out if my tumor margins were clear after surgery. I experienced that uncertainty and dread as a patient, so I became hugely sensitized to our mission.”

The approach Lumicell’s founders eventually settled on begins two to six hours before surgery, when patients receive the optical imaging agent through an IV. Then, during surgery, surgeons use Lumicell’s handheld imaging device to scan the walls of the breast cavity. Lumicell’s cancer detection software shows spots that highlight regions suspected to contain residual cancer on the computer monitor, which the surgeon can then remove. The process adds less than 7 minutes on average to the procedure.

“The technology we developed allows the surgeon to scan the actual cavity, whereas pathology only looks at the lump removed, and [pathologists] make their assessment based on looking at about 1 or 2 percent of the surface area,” Lee says. “Not only are we detecting cancer that was left behind to potentially eliminate second surgeries, we are also, very importantly, finding cancer in some patients that wouldn't be found in pathology and may not generate a second surgery.”

Exploring other cancer types

Lumicell is currently exploring if its imaging agent is activated in other tumor types, including prostate, sarcoma, esophageal, gastric, and more.

Lee ran Lumicell between 2008 and 2020. After stepping down as CEO, he decided to return to MIT to get his PhD in neuroscience, a full 50 years since he earned his master’s. Shortly thereafter, Howard Hechler took over as Lumicell’s president and chief operating officer.

Looking back, Griffith credits MIT’s culture of learning for the formation of Lumicell.

“People like David [Lee] and Moungi care about solving problems,” Griffith says. “They’re technically brilliant, but they also love learning from other people, and that’s what makes makes MIT special. People are confident about what they know, but they are also comfortable in that they don’t know everything, which drives great collaboration. We work together so that the whole is bigger than the sum of the parts.”

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A new approach to modeling complex biological systems

Over the past two decades, new technologies have helped scientists generate a vast amount of biological data. Large-scale experiments in genomics, transcriptomics, proteomics, and cytometry can produce enormous quantities of data from a given cellular or multicellular system.

However, making sense of this information is not always easy. This is especially true when trying to analyze complex systems such as the cascade of interactions that occur when the immune system encounters a foreign pathogen.

MIT biological engineers have now developed a new computational method for extracting useful information from these datasets. Using their new technique, they showed that they could unravel a series of interactions that determine how the immune system responds to tuberculosis vaccination and subsequent infection.

This strategy could be useful to vaccine developers and to researchers who study any kind of complex biological system, says Douglas Lauffenburger, the Ford Professor of Engineering in the departments of Biological Engineering, Biology, and Chemical Engineering.

“We’ve landed on a computational modeling framework that allows prediction of effects of perturbations in a highly complex system, including multiple scales and many different types of components,” says Lauffenburger, the senior author of the new study.

Shu Wang, a former MIT postdoc who is now an assistant professor at the University of Toronto, and Amy Myers, a research manager in the lab of University of Pittsburgh School of Medicine Professor JoAnne Flynn, are the lead authors of a new paper on the work, which appears today in the journal Cell Systems.

Modeling complex systems

When studying complex biological systems such as the immune system, scientists can extract many different types of data. Sequencing cell genomes tells them which gene variants a cell carries, while analyzing messenger RNA transcripts tells them which genes are being expressed in a given cell. Using proteomics, researchers can measure the proteins found in a cell or biological system, and cytometry allows them to quantify a myriad of cell types present.

Using computational approaches such as machine learning, scientists can use this data to train models to predict a specific output based on a given set of inputs — for example, whether a vaccine will generate a robust immune response. However, that type of modeling doesn’t reveal anything about the steps that happen in between the input and the output.

“That AI approach can be really useful for clinical medical purposes, but it’s not very useful for understanding biology, because usually you’re interested in everything that’s happening between the inputs and outputs,” Lauffenburger says. “What are the mechanisms that actually generate outputs from inputs?”

To create models that can identify the inner workings of complex biological systems, the researchers turned to a type of model known as a probabilistic graphical network. These models represent each measured variable as a node, generating maps of how each node is connected to the others.

Probabilistic graphical networks are often used for applications such as speech recognition and computer vision, but they have not been widely used in biology.

Lauffenburger’s lab has previously used this type of model to analyze intracellular signaling pathways, which required analyzing just one kind of data. To adapt this approach to analyze many datasets at once, the researchers applied a mathematical technique that can filter out any correlations between variables that are not directly affecting each other. This technique, known as graphical lasso, is an adaptation of the method often used in machine learning models to strip away results that are likely due to noise.

“With correlation-based network models generally, one of the problems that can arise is that everything seems to be influenced by everything else, so you have to figure out how to strip down to the most essential interactions,” Lauffenburger says. “Using probabilistic graphical network frameworks, one can really boil down to the things that are most likely to be direct and throw out the things that are most likely to be indirect.”

Mechanism of vaccination

To test their modeling approach, the researchers used data from studies of a tuberculosis vaccine. This vaccine, known as BCG, is an attenuated form of Mycobacterium bovis. It is used in many countries where TB is common but isn’t always effective, and its protection can weaken over time.

In hopes of developing more effective TB protection, researchers have been testing whether delivering the BCG vaccine intravenously or by inhalation might provoke a better immune response than injecting it. Those studies, performed in animals, found that the vaccine did work much better when given intravenously. In the MIT study, Lauffenburger and his colleagues attempted to discover the mechanism behind this success.

The data that the researchers examined in this study included measurements of about 200 variables, including levels of cytokines, antibodies, and different types of immune cells, from about 30 animals.

The measurements were taken before vaccination, after vaccination, and after TB infection. By analyzing the data using their new modeling approach, the MIT team was able to determine the steps needed to generate a strong immune response. They showed that the vaccine stimulates a subset of T cells, which produce a cytokine that activates a set of B cells that generate antibodies targeting the bacterium.

“Almost like a roadmap or a subway map, you could find what were really the most important paths. Even though a lot of other things in the immune system were changing one way or another, they were really off the critical path and didn't matter so much,” Lauffenburger says.

The researchers then used the model to make predictions for how a specific disruption, such as suppressing a subset of immune cells, would affect the system. The model predicted that if B cells were nearly eliminated, there would be little impact on the vaccine response, and experiments showed that prediction was correct.

This modeling approach could be used by vaccine developers to predict the effect their vaccines may have, and to make tweaks that would improve them before testing them in humans. Lauffenburger’s lab is now using the model to study the mechanism of a malaria vaccine that has been given to children in Kenya, Ghana, and Malawi over the past few years.

His lab is also using this type of modeling to study the tumor microenvironment, which contains many types of immune cells and cancerous cells, in hopes of predicting how tumors might respond to different kinds of treatment.

The research was funded by the National Institute of Allergy and Infectious Diseases.

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Despite its impressive output, generative AI doesn’t have a coherent understanding of the world

Large language models can do impressive things, like write poetry or generate viable computer programs, even though these models are trained to predict words that come next in a piece of text.

Such surprising capabilities can make it seem like the models are implicitly learning some general truths about the world.

But that isn’t necessarily the case, according to a new study. The researchers found that a popular type of generative AI model can provide turn-by-turn driving directions in New York City with near-perfect accuracy — without having formed an accurate internal map of the city.

Despite the model’s uncanny ability to navigate effectively, when the researchers closed some streets and added detours, its performance plummeted.

When they dug deeper, the researchers found that the New York maps the model implicitly generated had many nonexistent streets curving between the grid and connecting far away intersections.

This could have serious implications for generative AI models deployed in the real world, since a model that seems to be performing well in one context might break down if the task or environment slightly changes.

“One hope is that, because LLMs can accomplish all these amazing things in language, maybe we could use these same tools in other parts of science, as well. But the question of whether LLMs are learning coherent world models is very important if we want to use these techniques to make new discoveries,” says senior author Ashesh Rambachan, assistant professor of economics and a principal investigator in the MIT Laboratory for Information and Decision Systems (LIDS).

Rambachan is joined on a paper about the work by lead author Keyon Vafa, a postdoc at Harvard University; Justin Y. Chen, an electrical engineering and computer science (EECS) graduate student at MIT; Jon Kleinberg, Tisch University Professor of Computer Science and Information Science at Cornell University; and Sendhil Mullainathan, an MIT professor in the departments of EECS and of Economics, and a member of LIDS. The research will be presented at the Conference on Neural Information Processing Systems.

New metrics

The researchers focused on a type of generative AI model known as a transformer, which forms the backbone of LLMs like GPT-4. Transformers are trained on a massive amount of language-based data to predict the next token in a sequence, such as the next word in a sentence.

But if scientists want to determine whether an LLM has formed an accurate model of the world, measuring the accuracy of its predictions doesn’t go far enough, the researchers say.

For example, they found that a transformer can predict valid moves in a game of Connect 4 nearly every time without understanding any of the rules.

So, the team developed two new metrics that can test a transformer’s world model. The researchers focused their evaluations on a class of problems called deterministic finite automations, or DFAs. 

A DFA is a problem with a sequence of states, like intersections one must traverse to reach a destination, and a concrete way of describing the rules one must follow along the way.

They chose two problems to formulate as DFAs: navigating on streets in New York City and playing the board game Othello.

“We needed test beds where we know what the world model is. Now, we can rigorously think about what it means to recover that world model,” Vafa explains.

The first metric they developed, called sequence distinction, says a model has formed a coherent world model it if sees two different states, like two different Othello boards, and recognizes how they are different. Sequences, that is, ordered lists of data points, are what transformers use to generate outputs.

The second metric, called sequence compression, says a transformer with a coherent world model should know that two identical states, like two identical Othello boards, have the same sequence of possible next steps.

They used these metrics to test two common classes of transformers, one which is trained on data generated from randomly produced sequences and the other on data generated by following strategies.

Incoherent world models

Surprisingly, the researchers found that transformers which made choices randomly formed more accurate world models, perhaps because they saw a wider variety of potential next steps during training. 

“In Othello, if you see two random computers playing rather than championship players, in theory you’d see the full set of possible moves, even the bad moves championship players wouldn’t make,” Vafa explains.

Even though the transformers generated accurate directions and valid Othello moves in nearly every instance, the two metrics revealed that only one generated a coherent world model for Othello moves, and none performed well at forming coherent world models in the wayfinding example.

The researchers demonstrated the implications of this by adding detours to the map of New York City, which caused all the navigation models to fail.

“I was surprised by how quickly the performance deteriorated as soon as we added a detour. If we close just 1 percent of the possible streets, accuracy immediately plummets from nearly 100 percent to just 67 percent,” Vafa says.

When they recovered the city maps the models generated, they looked like an imagined New York City with hundreds of streets crisscrossing overlaid on top of the grid. The maps often contained random flyovers above other streets or multiple streets with impossible orientations.

These results show that transformers can perform surprisingly well at certain tasks without understanding the rules. If scientists want to build LLMs that can capture accurate world models, they need to take a different approach, the researchers say.

“Often, we see these models do impressive things and think they must have understood something about the world. I hope we can convince people that this is a question to think very carefully about, and we don’t have to rely on our own intuitions to answer it,” says Rambachan.

In the future, the researchers want to tackle a more diverse set of problems, such as those where some rules are only partially known. They also want to apply their evaluation metrics to real-world, scientific problems.

This work is funded, in part, by the Harvard Data Science Initiative, a National Science Foundation Graduate Research Fellowship, a Vannevar Bush Faculty Fellowship, a Simons Collaboration grant, and a grant from the MacArthur Foundation.

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Bridging Talents and Opportunities Forum connects high school and college students with STEAM leaders and resources

Bridging Talents and Opportunities (BTO) held its second annual forum at the Stratton Student Center at MIT Oct. 11-12. The two-day event gathered over 500 participants, including high school students and their families, undergraduate students, professors, and leaders across STEAM (science, technology, engineering, arts, and mathematics) fields.

The forum sought to empower talented students from across the United States and Latin America to dream big and pursue higher education, demonstrating that access to prestigious institutions like MIT is possible regardless of socioeconomic barriers. The event featured inspirational talks from world-renowned scientists, innovators, entrepreneurs, social leaders, and major figures in entertainment — from Nobel laureate Rigoberta Menchú Tum to musician and producer Emilio Estefan, and more.

“Our initiative is committed to building meaningful connections among talented young individuals, their families, foundations, and leaders in science, art, mathematics, and technology,” says Ronald Garcia Ruiz, the Thomas A. Frank Career Development Assistant Professor of Physics at MIT and an organizer of the forum. “Recognizing that talent is universal but opportunities are often confined to select sectors of society, we are dedicated to bridging this gap. BTO provides a platform for sharing inspiring stories and offering support to promising young talents, empowering them to seize the diverse opportunities that await them.”

During their talks and panel discussions, speakers shared their insight into topics such as access to STEAM education, overcoming challenges and socioeconomic barriers, and strategies for fostering inclusion in STEAM fields. Students also had the opportunity to network with industry leaders and professionals, building connections to foster future collaborations.

Attendees also participated in hands-on scientific demonstrations, interaction with robots, and tours of MIT labs, providing a view of cutting-edge scientific research. The event also included musical performances from Latin American students from Berklee College of Music.

“I was thrilled to see the enthusiasm of young people and their parents and to be inspired by the great life stories of accomplished scientists and individuals from other fields making a positive impact in the real world,” says Edwin Pedrozo Peñafiel, assistant professor of physics at the University of Florida and an organizer. “This is why I strongly believe that representation matters.”

Welcoming a Nobel laureate

The first day of the forum opened with the welcoming words from Nergis Mavalvala, dean of the School of Science, and Boleslaw Wyslouch, director of the Laboratory for Nuclear Science and the MIT Bates Research and Engineering Center, and concluded with a keynote address by human rights activist Rigoberta Menchú Tum, 1992 Nobel Peace laureate and founder of the Rigoberta Menchú Tum Foundation. Reflecting upon Indigenous perspectives on science, she emphasized the importance of maintaining a humanistic perspective in scientific discovery. “My struggle has been one of constructing a humanistic perspective … that science, technology … are products of the strength of human beings,” Menchú remarked. She also shared her extraordinary story, encouraging students to persevere no matter the obstacles.

Diana Grass, a PhD Student in the Harvard-MIT Health Sciences and Technology program and organizer, shares, “As a woman in science and a first-generation student, I’ve experienced firsthand the impact of breaking barriers and the importance of representation. At Bridging Talents and Opportunities (BTO), we are shaping a future where opportunities are available to all. Seeing students from disadvantaged backgrounds, along with their parents, engage with some of today’s most influential scientists and leaders — who shared their own stories of resilience — was both inspiring and transformative. It ignited crucial conversations about how interdisciplinary collaboration in STEAM, grounded in humanity, is essential for tackling the critical challenges of our era.”

Power of the Arts

The second day concluded with a panel on “The Power of the Arts,” featuring actor, singer, and songwriter Carlos Ponce, as well as musician and producer Emilio Estefan. They were joined by journalist and author Luz María Doria, who moderated the discussion. Throughout the panel, the speakers recounted their inspiring journeys toward success in the entertainment industry. “This forum reaffirmed our commitment to bridging talent with opportunity,” says Ponce. “The energy and engagement from students, families, and speakers were incredible, fostering a space of learning, empowerment, and possibility.”

During the forum, a two-hour workshop was held that brought together scientists, nonprofit foundations, and business leaders to discuss concrete proposals for creating opportunities for young talents. In this workshop, they had the opportunity to share their ideas with one another. Key ideas and final takeaways from the workshop included developing strategic programs to match talented young students with mentors from diverse backgrounds who can serve as role models, better utilization of existing programs supporting underserved populations, dissemination of information about such programs, ideas to improve financial support for students pursuing education, and fostering extended collaborations between the three groups involved in the workshop.

Maria Angélica Cuellar, CEO of Incontact Group and a BTO organizer, says, “The event was absolutely spectacular and exceeded our expectations. We not only brought together leaders making a global impact in STEAM and business, but also secured financial commitments to support young talents. Through media coverage and streaming, our message reached every corner of the world, especially Latin America and the U.S. I’m deeply grateful for the commitment of each speaker and for the path now open to turn this dream of connecting stakeholders into tangible results and actions. An exciting challenge lies ahead, driving us to work even harder to create opportunities for these talented young people.”

“Bridging Talents and Opportunities was a unique event that brought together students, parents, professors, and leaders in different fields in a relatable and inspiring environment,” says Sebastián Ruiz Lopera, a PhD candidate in the Department of Electrical Engineering and Computer Science and an organizer. “Every speaker, panelist, and participant shared a story of resilience and passion that will motivate the next generation of young talents from disadvantaged backgrounds to become the new leaders and stakeholders.”

The 2024 BTO forum was made possible with the support of the Latinx Graduate Student Association at MIT, Laboratory of Nuclear Science, MIT MLK Scholars Program, Institute Community and Equity Office, the School of Science, the U.S. Department of Energy, University of Florida, CHN, JGMA Architects, Berklee College of Music, and the Harvard Colombian Student Society.

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Artist and designer Es Devlin awarded Eugene McDermott Award in the Arts at MIT

Artist and designer Es Devlin is the recipient of the 2025 Eugene McDermott Award in the Arts at MIT. The $100,000 prize, to be awarded at a gala in her honor, also includes an artist residency at MIT in spring 2025, during which Es Devlin will present her work in a lecture open to the public on May 1, 2025. 

Devlin’s work explores biodiversity, linguistic diversity, and collective AI-generated poetry, all areas that also are being explored within the MIT community. She is known for public art and installations at major museums such as the Tate Modern, kinetic stage designs for the Metropolitan Opera, the Super Bowl, and the Olympics, as well as monumental stage sculptures for large-scale stadium concerts.

“I am always most energized by works I have not yet made, so I am immensely grateful to have this trust and investment in ideas I’ve yet to conceive,” says Devlin. “I’m honored to receive an award that has been granted to so many of my heroes, and look forward to collaborating closely with the brilliant minds at MIT.”

“We look forward to presenting Es Devlin with MIT’s highest award in the arts. Her work will be an inspiration for our students studying the visual arts, theater, media, and design. Her interest in AI and the arts dovetails with a major initiative at MIT to address the societal impact of GenAI [generative artificial intelligence],” says MIT vice provost and Ford International Professor of History Philip S. Khoury. “With a new performing arts center opening this winter and a campus-wide arts festival taking place this spring, there could not be a better moment to expose MIT’s creative community to Es Devlin’s extraordinary artistic practice.”

The Eugene McDermott Award in the Arts at MIT recognizes innovative artists working in any field or cross-disciplinary activity. The $100,000 prize represents an investment in the recipient’s future creative work, rather than a prize for a particular project or lifetime of achievement. The official announcement was made at the Council for the Arts at MIT’s 51st annual meeting on Oct. 24. Since it was established in 1974, the award has been bestowed upon 38 individuals who work in performing, visual, and media arts, as well as authors, art historians, and patrons of the arts. Past recipients include Santiago Calatrava, Gustavo Dudamel, Olafur Eliasson, Robert Lepage, Audra McDonald, Suzan-Lori Parks, Bill Viola, and Pamela Z, among others.

A distinctive feature of the award is a short residency at MIT, which includes a public presentation of the artist’s work, substantial interaction with students and faculty, and a gala that convenes national and international leaders in the arts. The goal of the residency is to provide the recipient with unparalleled access to the creative energy and cutting-edge research at the Institute and to develop mutually enlightening relationships in the MIT community.

The Eugene McDermott Award in the Arts at MIT was established in 1974 by Margaret McDermott (1912-2018) in honor of her husband, Eugene McDermott (1899-1973), a co-founder of Texas Instruments and longtime friend and benefactor of MIT. The award is presented by the Council for the Arts at MIT.

The award is bestowed upon individuals whose artistic trajectory and body of work have achieved the highest distinction in their field and indicate they will remain leaders for years to come. The McDermott Award reflects MIT’s commitment to risk-taking, problem-solving, and connecting creative minds across disciplines.

Es Devlin, born in London in 1971, views an audience as a temporary society and often invites public participation in communal choral works. Her canvas ranges from public sculptures and installations at Tate Modern, V&A, Serpentine, Imperial War Museum, and Lincoln Center, to kinetic stage designs at the Royal Opera House, the National Theatre, and the Metropolitan Opera, as well as Olympic ceremonies, Super Bowl halftime shows, and monumental illuminated stage sculptures for large-scale stadium concerts.

Devlin is the subject of a major monographic book, “An Atlas of Es Devlin,” described by Thames and Hudson as their most intricate and sculptural publication to date, and a retrospective exhibition at the Cooper Hewitt Smithsonian Design Museum in New York. In 2020, she became the first female architect of the U.K. Pavilion at a World Expo, conceiving a building which used AI to co-author poetry with visitors on its 20-meter diameter facade. Her practice was the subject of the 2015 Netflix documentary series “Abstract: The Art of Design.” She is a fellow of the Royal Academy of Music, University of the Arts London, and a Royal Designer for Industry at the Royal Society of Arts. She has been awarded the London Design Medal, three Olivier Awards, a Tony Award, an Ivor Novello Award, doctorates from the Universities of Bristol and Kent, and a Commander of the Order of the British Empire award.

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Nanoscale transistors could enable more efficient electronics

Silicon transistors, which are used to amplify and switch signals, are a critical component in most electronic devices, from smartphones to automobiles. But silicon semiconductor technology is held back by a fundamental physical limit that prevents transistors from operating below a certain voltage.

This limit, known as “Boltzmann tyranny,” hinders the energy efficiency of computers and other electronics, especially with the rapid development of artificial intelligence technologies that demand faster computation.

In an effort to overcome this fundamental limit of silicon, MIT researchers fabricated a different type of three-dimensional transistor using a unique set of ultrathin semiconductor materials.

Their devices, featuring vertical nanowires only a few nanometers wide, can deliver performance comparable to state-of-the-art silicon transistors while operating efficiently at much lower voltages than conventional devices.

“This is a technology with the potential to replace silicon, so you could use it with all the functions that silicon currently has, but with much better energy efficiency,” says Yanjie Shao, an MIT postdoc and lead author of a paper on the new transistors.

The transistors leverage quantum mechanical properties to simultaneously achieve low-voltage operation and high performance within an area of just a few square nanometers. Their extremely small size would enable more of these 3D transistors to be packed onto a computer chip, resulting in fast, powerful electronics that are also more energy-efficient.

“With conventional physics, there is only so far you can go. The work of Yanjie shows that we can do better than that, but we have to use different physics. There are many challenges yet to be overcome for this approach to be commercial in the future, but conceptually, it really is a breakthrough,” says senior author Jesús del Alamo, the Donner Professor of Engineering in the MIT Department of Electrical Engineering and Computer Science (EECS).

They are joined on the paper by Ju Li, the Tokyo Electric Power Company Professor in Nuclear Engineering and professor of materials science and engineering at MIT; EECS graduate student Hao Tang; MIT postdoc Baoming Wang; and professors Marco Pala and David Esseni of the University of Udine in Italy. The research appears today in Nature Electronics.

Surpassing silicon

In electronic devices, silicon transistors often operate as switches. Applying a voltage to the transistor causes electrons to move over an energy barrier from one side to the other, switching the transistor from “off” to “on.” By switching, transistors represent binary digits to perform computation.

A transistor’s switching slope reflects the sharpness of the “off” to “on” transition. The steeper the slope, the less voltage is needed to turn on the transistor and the greater its energy efficiency.

But because of how electrons move across an energy barrier, Boltzmann tyranny requires a certain minimum voltage to switch the transistor at room temperature.

To overcome the physical limit of silicon, the MIT researchers used a different set of semiconductor materials — gallium antimonide and indium arsenide — and designed their devices to leverage a unique phenomenon in quantum mechanics called quantum tunneling.

Quantum tunneling is the ability of electrons to penetrate barriers. The researchers fabricated tunneling transistors, which leverage this property to encourage electrons to push through the energy barrier rather than going over it.

“Now, you can turn the device on and off very easily,” Shao says.

But while tunneling transistors can enable sharp switching slopes, they typically operate with low current, which hampers the performance of an electronic device. Higher current is necessary to create powerful transistor switches for demanding applications.

Fine-grained fabrication

Using tools at MIT.nano, MIT’s state-of-the-art facility for nanoscale research, the engineers were able to carefully control the 3D geometry of their transistors, creating vertical nanowire heterostructures with a diameter of only 6 nanometers. They believe these are the smallest 3D transistors reported to date.

Such precise engineering enabled them to achieve a sharp switching slope and high current simultaneously. This is possible because of a phenomenon called quantum confinement.

Quantum confinement occurs when an electron is confined to a space that is so small that it can’t move around. When this happens, the effective mass of the electron and the properties of the material change, enabling stronger tunneling of the electron through a barrier.

Because the transistors are so small, the researchers can engineer a very strong quantum confinement effect while also fabricating an extremely thin barrier.

“We have a lot of flexibility to design these material heterostructures so we can achieve a very thin tunneling barrier, which enables us to get very high current,” Shao says.

Precisely fabricating devices that were small enough to accomplish this was a major challenge.

“We are really into single-nanometer dimensions with this work. Very few groups in the world can make good transistors in that range. Yanjie is extraordinarily capable to craft such well-functioning transistors that are so extremely small,” says del Alamo.

When the researchers tested their devices, the sharpness of the switching slope was below the fundamental limit that can be achieved with conventional silicon transistors. Their devices also performed about 20 times better than similar tunneling transistors.

“This is the first time we have been able to achieve such sharp switching steepness with this design,” Shao adds.

The researchers are now striving to enhance their fabrication methods to make transistors more uniform across an entire chip. With such small devices, even a 1-nanometer variance can change the behavior of the electrons and affect device operation. They are also exploring vertical fin-shaped structures, in addition to vertical nanowire transistors, which could potentially improve the uniformity of devices on a chip.

“This work definitively steps in the right direction, significantly improving the broken-gap tunnel field effect transistor (TFET) performance. It demonstrates steep-slope together with a record drive-current. It highlights the importance of small dimensions, extreme confinement, and low-defectivity materials and interfaces in the fabricated broken-gap TFET. These features have been realized through a well-mastered and nanometer-size-controlled process,” says Aryan Afzalian, a principal member of the technical staff at the nanoelectronics research organization imec, who was not involved with this work.

This research is funded, in part, by Intel Corporation.

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Finding a sweet spot between radical and relevant

While working as a lecturer in MIT’s Department of Architecture, Skylar Tibbits SM ’10 was also building art installations in galleries all over the world. Most of these installations featured complex structures created from algorithmically designed and computationally fabricated parts, building off Tibbits’ graduate work at the Institute.

Late one night in 2011 he was working with his team for hours — painstakingly riveting and bolting together thousands of tiny parts — to install a corridor-spanning work called VoltaDom at MIT for the Institute’s 150th anniversary celebration.

“There was a moment during the assembly when I realized this was the opposite of what I was interested in. We have elegant code for design and fabrication, but we didn’t have elegant code for construction. How can we promote things to build themselves? That is where the research agenda for my lab really came into being,” he says.

Tibbits, now a tenured associate professor of design research, co-directs the Self-Assembly Lab in the Department of Architecture, where he and his collaborators study self-organizing systems, programmable materials, and transformable structures that respond to their environments.

His research covers a diverse range of projects, including furniture that autonomously assembles from parts dropped into a water tank, rapid 3D printing with molten aluminum, and programmable textiles that sense temperature and automatically adjust to cool the body.

“If you were to ask someone on the street about self-assembly, they probably think of IKEA. But that is not what we mean. I am not the ‘self’ that is going to assemble something. Instead, the parts should build themselves,” he says.

Creative foundations

As a child growing up near Philadelphia, the hands-on Tibbits did like to build things manually. He took a keen interest in art and design, inspired by his aunt and uncle who were both professional artists, and his grandfather, who worked as an architect.

Tibbits decided to study architecture at Philadelphia University (now called Thomas Jefferson University) and chose the institution based on his grandfather’s advice to pick a college that was strong in design.

“At that time, I didn’t really know what that meant,” he recalls, but it was good advice. Being able to think like a designer helped form his career trajectory and continues to fuel the work he and his collaborators do in the Self-Assembly Lab.

While he was studying architecture, the digitization boom was changing many aspects of the field. Initially he and his classmates were drafting by hand, but software and digital fabrication equipment soon overtook traditional methods.

Wanting to get ahead of the curve, Tibbits taught himself to code. He used equipment in a sign shop owned by the father of classmate Jared Laucks (who is now a research scientist and co-director of the Self-Assembly Lab) to digitally fabricate objects before their school had the necessary machines.

Looking to further his education, Tibbits decided to pursue graduate studies at MIT because he wanted to learn computation from full-time computer scientists rather than architects teaching digital tools.

“I wanted to learn a different discipline and really enter a different world. That is what brought me to MIT, and I never left,” he says.

Tibbits earned dual master’s degrees in computer science and design and computation, delving deeper the theory of computation and the question of what it means to compute. He became interested in the challenge of embedding information into our everyday world.

One of his most influential experiences as a graduate student was a series of projects he worked on in the Center for Bits and Atoms that involved building reconfigurable robots.

“I wanted to figure out how to program materials to change shape, change properties, or assemble themselves,” he says.

He was pondering these questions as he graduated from MIT and joined the Institute as a lecturer, teaching studios and labs in the Department of Architecture. Eventually, he decided to become a research scientist so he could run a lab of his own.

“I had some prior experience in architectural practice, but I was really fascinated by what I was doing at MIT. It seemed like there were a million things I wanted to work on, so staying here to teach and do research was the perfect opportunity,” he says.

Launching a lab

As he was forming the Self-Assembly Lab, Tibbits had a chance meeting with someone wearing a Stratasys t-shirt at Flour Bakery and Café, near campus. (Stratasys is a manufacturer of 3D printers.)

A lightbulb went off in his head.

“I asked them, why can’t I print a material that behaves like a robot and just walks off the machine? Why can’t I print robots without adding electronics or motors or wires or mechanisms?” he says.

That idea gave rise to one of his lab’s earliest projects: 4D printing. The process involves using a multimaterial 3D printer to print objects designed to sense, actuate, and transform themselves over time.

To accomplish this, Tibbits and his team link material properties with a certain activation energy. For instance, moisture will transform cellulose, and temperature will activate polymers. The researchers fabricate materials into certain geometries so they can leverage these activation energies to transform the material in predictable and precise ways.

“It is almost like making everything a ‘smart’ material,” he says.

The lab’s initial 4D printing work has evolved to include different materials, such as textiles, and has led the team to invent new printing processes, such as rapid liquid printing and liquid metal printing.

They have used 4D printing in many applications, often working with industry partners. For instance, they collaborated with Airbus to develop thin blades that can fold and curl themselves to control the airflow to an airplane’s engine.

On an even greater scale, the team also embarked on a multiyear project in 2015 with the organization Invena in the Maldives to leverage self-assembly to “grow” small islands and rebuild beaches, which could help protect this archipelago from rising seas.

To do this, they fabricate submersible devices that, based on their geometry and the natural forces of the ocean like wave energy and tides, promote the accumulation of sand in specific areas to become sand bars.

They have now created nine field installations in the Maldives, the largest of which measures approximately 60 square meters. The end goal is to promote the self-organization of sand into protective barriers against sea level rise, rebuild beaches to fight erosion, and eliminate the need to dredge for land reclamation.

They are now working on similar projects in Iceland with J. Jih, associate professor of the practice in architectural design at MIT, looking at mountain erosion and volcanic lava flows, and Tibbits foresees many potential applications for self-assembly in natural environments.

“There are almost an unlimited number of places, and an unlimited number of forces that we could harness to tackle big, important problems, whether it is beach erosion or protecting communities from volcanoes,” he says.

Blending the radical and the relevant

Self-organizing sand bars are a prime example of a project that combines a radical idea with a relevant application, Tibbits says. He strives to find projects that strike such a balance and don’t only push boundaries without solving a real-world problem.

Working with brilliant and passionate researchers in the Self-Assembly Lab helps Tibbits stay inspired and creative as they launch new projects aimed at tackling big problems.

He feels especially passionate about his role as a teacher and mentor. In addition to teaching three or four courses each year, he directs the undergraduate design program at MIT.

Any MIT student can choose to major or minor in design, and the program focuses on many aspects and types of design to give students a broad foundation they can apply in their future careers.

“I am passionate about creating polymath designers at MIT who can apply design to any other discipline, and vice-versa. I think my lab is the ethos of that, where we take creative approaches and apply them to research, and where we apply new principles from different disciplines to create new forms of design,” he says.

Outside the lab and classroom, Tibbits often finds inspiration by spending time on the water. He lives at the beach on the North Shore of Massachusetts and is a surfer, a hobby he had dabbled in during his youth, but which really took hold after he moved to the Bay State for graduate school.

“It is such an amazing sport to keep you in tune with the forces of the ocean. You can’t control the environment, so to ride a wave you have to find a way to harness it,” he says.

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Smart handling of neutrons is crucial to fusion power success

In fall 2009, when Ethan Peterson ’13 arrived at MIT as an undergraduate, he already had some ideas about possible career options. He’d always liked building things, even as a child, so he imagined his future work would involve engineering of some sort. He also liked physics. And he’d recently become intent on reducing our dependence on fossil fuels and simultaneously curbing greenhouse gas emissions, which made him consider studying solar and wind energy, among other renewable sources.

Things crystallized for him in the spring semester of 2010, when he took an introductory course on nuclear fusion, taught by Anne White, during which he discovered that when a deuterium nucleus and a tritium nucleus combine to produce a helium nucleus, an energetic (14 mega electron volt) neutron — traveling at one-sixth the speed of light — is released. Moreover, 10²⁰ (100 billion billion) of these neutrons would be produced every second that a 500-megawatt fusion power plant operates. “It was eye-opening for me to learn just how energy-dense the fusion process is,” says Peterson, who became the Class of 1956 Career Development Professor of nuclear science and engineering in July 2024. “I was struck by the richness and interdisciplinary nature of the fusion field. This was an engineering discipline where I could apply physics to solve a real-world problem in a way that was both interesting and beautiful.”

He soon became a physics and nuclear engineering double major, and by the time he graduated from MIT in 2013, the U.S. Department of Energy (DoE) had already decided to cut funding for MIT’s Alcator C-Mod fusion project. In view of that facility’s impending closure, Peterson opted to pursue graduate studies at the University of Wisconsin. There, he acquired a basic science background in plasma physics, which is central not only to nuclear fusion but also to astrophysical phenomena such as the solar wind.

When Peterson received his PhD from Wisconsin in 2019, nuclear fusion had rebounded at MIT with the launch, a year earlier, of the SPARC project — a collaborative effort being carried out with the newly founded MIT spinout Commonwealth Fusion Systems. He returned to his alma mater as a postdoc and then a research scientist in the Plasma Science and Fusion Center, taking his time, at first, to figure out how to best make his mark in the field.

Minding your neutrons

Around that time, Peterson was participating in a community planning process, sponsored by the DoE, that focused on critical gaps that needed to be closed for a successful fusion program. In the course of these discussions, he came to realize that inadequate attention had been paid to the handling of neutrons, which carry 80 percent of the energy coming out of a fusion reaction — energy that needs to be harnessed for electrical generation. However, these neutrons are so energetic that they can penetrate through many tens of centimeters of material, potentially undermining the structural integrity of components and damaging vital equipment such as superconducting magnets. Shielding is also essential for protecting humans from harmful radiation.

One goal, Peterson says, is to minimize the number of neutrons that escape and, in so doing, to reduce the amount of lost energy. A complementary objective, he adds, “is to get neutrons to deposit heat where you want them to and to stop them from depositing heat where you don’t want them to.” These considerations, in turn, can have a profound influence on fusion reactor design. This branch of nuclear engineering, called neutronics — which analyzes where neutrons are created and where they end up going — has become Peterson’s specialty.

It was never a high-profile area of research in the fusion community — as plasma physics, for example, has always garnered more of the spotlight and more of the funding. That’s exactly why Peterson has stepped up. “The impacts of neutrons on fusion reactor design haven’t been a high priority for a long time,” he says. “I felt that some initiative needed to be taken,” and that prompted him to make the switch from plasma physics to neutronics. It has been his principal focus ever since — as a postdoc, a research scientist, and now as a faculty member.

A code to design by

The best way to get a neutron to transfer its energy is to make it collide with a light atom. Lithium, with an atomic number of three, or lithium-containing materials are normally good choices — and necessary for producing tritium fuel. The placement of lithium “blankets,” which are intended to absorb energy from neutrons and produce tritium, “is a critical part of the design of fusion reactors,” Peterson says. High-density materials, such as lead and tungsten, can be used, conversely, to block the passage of neutrons and other types of radiation. “You might want to layer these high- and low-density materials in a complicated way that isn’t immediately intuitive” he adds. Determining which materials to put where — and of what thickness and mass — amounts to a tricky optimization problem, which will affect the size, cost, and efficiency of a fusion power plant.

To that end, Peterson has developed modelling tools that can make analyses of these sorts easier and faster, thereby facilitating the design process. “This has traditionally been the step that takes the longest time and causes the biggest holdups,” he says. The models and algorithms that he and his colleagues are devising are general enough, moreover, to be compatible with a diverse range of fusion power plant concepts, including those that use magnets or lasers to confine the plasma.

Now that he’s become a professor, Peterson is in a position to introduce more people to nuclear engineering, and to neutronics in particular. “I love teaching and mentoring students, sharing the things I’m excited about,” he says. “I was inspired by all the professors I had in physics and nuclear engineering at MIT, and I hope to give back to the community in the same way.”

He also believes that if you are going to work on fusion, there is no better place to be than MIT, “where the facilities are second-to-none. People here are extremely innovative and passionate. And the sheer number of people who excel in their fields is staggering.” Great ideas can sometimes be sparked by off-the-cuff conversations in the hallway — something that happens more frequently than you expect, Peterson remarks. “All of these things taken together makes MIT a very special place.”

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3 Questions: Can we secure a sustainable supply of nickel?

As the world strives to cut back on carbon emissions, demand for minerals and metals needed for clean energy technologies is growing rapidly, sometimes straining existing supply chains and harming local environments. In a new study published today in Joule, Elsa Olivetti, a professor of materials science and engineering and director of the Decarbonizing Energy and Industry mission within MIT’s Climate Project, along with recent graduates Basuhi Ravi PhD ’23 and Karan Bhuwalka PhD ’24 and nine others, examine the case of nickel, which is an essential element for some electric vehicle batteries and parts of some solar panels and wind turbines.

How robust is the supply of this vital metal, and what are the implications of its extraction for the local environments, economies, and communities in the places where it is mined? MIT News asked Olivetti, Ravi, and Bhuwalka to explain their findings.

Q: Why is nickel becoming more important in the clean energy economy, and what are some of the potential issues in its supply chain?

Olivetti: Nickel is increasingly important for its role in EV batteries, as well as other technologies such as wind and solar. For batteries, high-purity nickel sulfate is a key input to the cathodes of EV batteries, which enables high energy density in batteries and increased driving range for EVs. As the world transitions away from fossil fuels, the demand for EVs, and consequently for nickel, has increased dramatically and is projected to continue to do so.

The nickel supply chain for battery-grade nickel sulfate includes mining nickel from ore deposits, processing it to a suitable nickel intermediary, and refining it to nickel sulfate. The potential issues in the supply chain can be broadly described as land use concerns in the mining stage, and emissions concerns in the processing stage. This is obviously oversimplified, but as a basic structure for our inquiry we thought about it this way. Nickel mining is land-intensive, leading to deforestation, displacement of communities, and potential contamination of soil and water resources from mining waste. In the processing step, the use of fossil fuels leads to direct emissions including particulate matter and sulfur oxides. In addition, some emerging processing pathways are particularly energy-intensive, which can double the carbon footprint of nickel-rich batteries compared to the current average.

Q: What is Indonesia’s role in the global nickel supply, and what are the consequences of nickel extraction there and in other major supply countries?

Ravi: Indonesia plays a critical role in nickel supply, holding the world's largest nickel reserves and supplying nearly half of the globally mined nickel in 2023. The country's nickel production has seen a remarkable tenfold increase since 2016. This production surge has fueled economic growth in some regions, but also brought notable environmental and social impacts to nickel mining and processing areas.

Nickel mining expansion in Indonesia has been linked to health impacts due to air pollution in the islands where nickel processing is prominent, as well as deforestation in some of the most biodiversity-rich locations on the planet. Reports of displacement of indigenous communities, land grabbing, water rights issues, and inadequate job quality in and around mines further highlight the social concerns and unequal distribution of burdens and benefits in Indonesia. Similar concerns exist in other major nickel-producing countries, where mining activities can negatively impact the environment, disrupt livelihoods, and exacerbate inequalities.

On a global scale, Indonesia’s reliance on coal-based energy for nickel processing, particularly in energy-intensive smelting and leaching of a clay-like material called laterite, results in a high carbon intensity for nickel produced in the region, compared to other major producing regions such as Australia.

Q: What role can industry and policymakers play in helping to meet growing demand while improving environmental safety?

Bhuwalka: In consuming countries, policies can foster “discerning demand,” which means creating incentives for companies to source nickel from producers that prioritize sustainability. This can be achieved through regulations that establish acceptable environmental footprints for imported materials, such as limits on carbon emissions from nickel production. For example, the EU’s Critical Raw Materials Act and the U.S. Inflation Reduction Act could be leveraged to promote responsible sourcing. Additionally, governments can use their purchasing power to favor sustainably produced nickel in public procurement, which could influence industry practices and encourage the adoption of sustainability standards.

On the supply side, nickel-producing countries like Indonesia can implement policies to mitigate the adverse environmental and social impacts of nickel extraction. This includes strengthening environmental regulations and enforcement to reduce the footprint of mining and processing, potentially through stricter pollution limits and responsible mine waste management. In addition, supporting community engagement, implementing benefit-sharing mechanisms, and investing in cleaner nickel processing technologies are also crucial.

Internationally, harmonizing sustainability standards and facilitating capacity building and technology transfer between developed and developing countries can create a level playing field and prevent unsustainable practices. Responsible investment practices by international financial institutions, favoring projects that meet high environmental and social standards, can also contribute to a stable and sustainable nickel supply chain.

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Revealing causal links in complex systems

Getting to the heart of causality is central to understanding the world around us. What causes one variable — be it a biological species, a voting region, a company stock, or a local climate — to shift from one state to another can inform how we might shape that variable in the future.

But tracing an effect to its root cause can quickly become intractable in real-world systems, where many variables can converge, confound, and cloud over any causal links.

Now, a team of MIT engineers hopes to provide some clarity in the pursuit of causality. They developed a method that can be applied to a wide range of situations to identify those variables that likely influence other variables in a complex system.

The method, in the form of an algorithm, takes in data that have been collected over time, such as the changing populations of different species in a marine environment. From those data, the method measures the interactions between every variable in a system and estimates the degree to which a change in one variable (say, the number of sardines in a region over time) can predict the state of another (such as the population of anchovy in the same region).

The engineers then generate a “causality map” that links variables that likely have some sort of cause-and-effect relationship. The algorithm determines the specific nature of that relationship, such as whether two variables are synergistic — meaning one variable only influences another if it is paired with a second variable — or redundant, such that a change in one variable can have exactly the same, and therefore redundant, effect as another variable.

The new algorithm can also make an estimate of “causal leakage,” or the degree to which a system’s behavior cannot be explained through the variables that are available; some unknown influence must be at play, and therefore, more variables must be considered.

“The significance of our method lies in its versatility across disciplines,” says Álvaro Martínez-Sánchez, a graduate student in MIT’s Department of Aeronautics and Astronautics (AeroAstro). “It can be applied to better understand the evolution of species in an ecosystem, the communication of neurons in the brain, and the interplay of climatological variables between regions, to name a few examples.”

For their part, the engineers plan to use the algorithm to help solve problems in aerospace, such as identifying features in aircraft design that can reduce a plane’s fuel consumption.

“We hope by embedding causality into models, it will help us better understand the relationship between design variables of an aircraft and how it relates to efficiency,” says Adrián Lozano-Durán, an associate professor in AeroAstro.

The engineers, along with MIT postdoc Gonzalo Arranz, have published their results in a study appearing today in Nature Communications.

Seeing connections

In recent years, a number of computational methods have been developed to take in data about complex systems and identify causal links between variables in the system, based on certain mathematical descriptions that should represent causality.

“Different methods use different mathematical definitions to determine causality,” Lozano-Durán notes. “There are many possible definitions that all sound ok, but they may fail under some conditions.”

In particular, he says that existing methods are not designed to tell the difference between certain types of causality. Namely, they don’t distinguish between a “unique” causality, in which one variable has a unique effect on another, apart from every other variable, from a “synergistic” or a “redundant” link. An example of a synergistic causality would be if one variable (say, the action of drug A) had no effect on another variable (a person’s blood pressure), unless the first variable was paired with a second (drug B).

An example of redundant causality would be if one variable (a student’s work habits) affect another variable (their chance of getting good grades), but that effect has the same impact as another variable (the amount of sleep the student gets).

“Other methods rely on the intensity of the variables to measure causality,” adds Arranz. “Therefore, they may miss links between variables whose intensity is not strong yet they are important.”

Messaging rates

In their new approach, the engineers took a page from information theory — the science of how messages are communicated through a network, based on a theory formulated by the late MIT professor emeritus Claude Shannon. The team developed an algorithm to evaluate any complex system of variables as a messaging network.

“We treat the system as a network, and variables transfer information to each other in a way that can be measured,” Lozano-Durán explains. “If one variable is sending messages to another, that implies it must have some influence. That’s the idea of using information propagation to measure causality.”

The new algorithm evaluates multiple variables simultaneously, rather than taking on one pair of variables at a time, as other methods do. The algorithm defines information as the likelihood that a change in one variable will also see a change in another. This likelihood — and therefore, the information that is exchanged between variables — can get stronger or weaker as the algorithm evaluates more data of the system over time.

In the end, the method generates a map of causality that shows which variables in the network are strongly linked. From the rate and pattern of these links, the researchers can then distinguish which variables have a unique, synergistic, or redundant relationship. By this same approach, the algorithm can also estimate the amount of “causality leak” in the system, meaning the degree to which a system’s behavior cannot be predicted based on the information available.

“Part of our method detects if there’s something missing,” Lozano-Durán says. “We don’t know what is missing, but we know we need to include more variables to explain what is happening.”

The team applied the algorithm to a number of benchmark cases that are typically used to test causal inference. These cases range from observations of predator-prey interactions over time, to measurements of air temperature and pressure in different geographic regions, and the co-evolution of multiple species in a marine environment. The algorithm successfully identified causal links in every case, compared with most methods that can only handle some cases.   

The method, which the team coined SURD, for Synergistic-Unique-Redundant Decomposition of causality, is available online for others to test on their own systems.

“SURD has the potential to drive progress across multiple scientific and engineering fields, such as climate research, neuroscience, economics, epidemiology, social sciences, and fluid dynamics, among others areas,” Martínez-Sánchez says.

This research was supported, in part, by the National Science Foundation.

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